Experimental analysis of drag reduction in the pipelines with response surface methodology

Karami, Hojat; Keyhani, Mohammad; Mowla, Dariush

doi:10.1016/j.petrol.2015.11.041

Cited by 31 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the desirable properties of second order polynomials, such as high predictability, robustness and simplicity, they are often used for estimating response value and determining the size of effects (Karami et al, 2016). The general form of a second order model is as below:…”

Section: Response Surface Methodologymentioning

confidence: 99%

Structural optimization of downhole oil-water separator

Zhao

Sun

2017

Journal of Petroleum Science and Engineering

View full text Add to dashboard Cite

： Downhole oil-water separator is the most important component of downhole oil-water separation system. Its structure has great effects on oil concentration of underflow (OCU) thus requiring further optimization. This work aims to propose an optimization method for the structural optimization of downhole oil-water separator that consists of two series de-oiling hydrocyclones. Firstly, the significant factors influencing OCU are identified by two-level Plackett-Burman Design (PBD) with twelve factors. Then the central levels of the five significant factors identified from PBD are determined by steepest ascent design. Secondly, response surface methodology (RSM) is used to establish the second order model between the OCU and five significant factors for the implementation of particle swarm optimization (PSO). Finally, the optimal structural parameters are obtained by PSO algorithm. Computational fluid dynamics (CFD) is employed to calculate the OCU for each particular case and analyze the separation performance variations before and after optimization. The simulation results show that compared with the original geometry and the best geometry in CCF design, the OCU of the optimized decreases. And the separation performance of the optimized geometry is remarkably improved for the oil droplets whose particle sizes are smaller than 35.78μm. Furthermore, laboratory experiments have been conducted to validate the proposed optimization method. The experimental results confirm that the OCU also reduces following the proposed optimization method. It can be summarized that PSO algorithm combined with PBD, steepest ascent design and RSM can be an effective method for the structural optimization of the downhole oil-water separator.

show abstract

Section: Response Surface Methodologymentioning

confidence: 99%

Structural optimization of downhole oil-water separator

Zhao

Sun

2017

Journal of Petroleum Science and Engineering

View full text Add to dashboard Cite

show abstract

“…This method was introduced by Box and Wilson 14 and then was utilized in various fields of engineering, for example see. [17][18][19] It is a combination of several mathematical and statistical techniques to aid in the analysis of historical data in problems where several control variables influence target response, particularly when the variables are highly interactive. It can be employed for (1) finding out the relationship between target response and control variables, (2) determining relative significance of control variables on response, and (3) optimizing response or determining optimum settings of control variables.…”

Section: Model Developmentmentioning

confidence: 99%

“…[15][16][17] Furthermore, it is demonstrated that RSM is especially useful when control variables are complicatedly interacted 18 or in the presence of abundant data. 19 Therefore, the aim of this study includes presenting a unified model for more accurate predicting the compressive strength of both circular and square/ rectangular FRP-confined concrete column. After collecting historical data, the RSM-induced model was presented, and sensitivity of output of the model to input parameters was then evaluated by sensitivity analysis.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven investigation of compressive strength of FRP‐confined concrete columns using a unified model based on RSM considering interactions between parameters

Vafaei,

Davoudi‐Kia,

Kutanaei

et al. 2024

Structural Concrete

View full text Add to dashboard Cite

In this study, a data‐driven model was developed to predict compressive strength of concrete columns after confining with Fiber‐Reinforced Polymer (FRP). Through application of Response Surface Method (RSM), interactions of input parameters are investigated in addition to their individual influence. Moreover, RSM is utilized in conjunction with unification to enhance model's accuracy. Furthermore, sensitivity analysis is employed together with RSM, to determine the effect size of statistically significant parameters. It is revealed that the real behavior of FRP confined concrete cannot be predicted by single‐parameter studies. Therefore, interaction terms should be included in the model to yield a more accurate prediction. Moreover, the model shows the highest sensitivity to FRP failure strength which this sensitivity decreases as corner radius increases. Therefore, regarding rounded‐corner sections, high improvements are achievable even by utilizing low strength FRPs. It is also shown that higher improvement may be limited due to premature debonding which elastic modulus mismatch between different phases is supposed to be responsible for it.

show abstract

“…Thus, in this work, 348 instances, including five input factors of Reynolds number, concentration, and type of drag-reducing agents, temperature, and type of pipe and drag reduction as the response parameter, construct the mentioned dataset. The database used to make machine learning models were obtained from previous studies that reviewed the percentage of drag reduction[2,24,74]. The following equations were used to calculate Reynolds number (Re), percentage drag reduction (DR%), respectively:…”

mentioning

confidence: 99%

Soft computing method for predicting pressure drop reduction in crude oil pipelines based on machine learning methods

Moayedi

Foong

Nguyen

2020

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

In the oil industry, the drag-reducing agent has been used to reduce turbulent friction of fluids. The main effort of this study is to examine the feasibility of four novel machine learning models, namely multilayer perceptron, M5Rules, decision table (DT), and trees M5P to estimate the percentage of drag reduction. Then, the mentioned methods are utilized to identify a relationship between the input and output parameters of the crude oil pipeline system. The parameter percentage of drag reduction was taken as the essential output. In contrast, the input parameters selected the flow rate of oil, polymer concentration, kind of polymer, temperature, as well as pipe diameter and roughness. The predicted results obtained by the tools mentioned above were evaluated according to several known statistical indices, namely coefficient of determination (R 2), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) as well as novel ranking systems of color intensity rating and total ranking method. The training and testing results of the DT learning method for the

show abstract

Experimental analysis of drag reduction in the pipelines with response surface methodology

Cited by 31 publications

References 21 publications

Structural optimization of downhole oil-water separator

Structural optimization of downhole oil-water separator

Data‐driven investigation of compressive strength of FRP‐confined concrete columns using a unified model based on RSM considering interactions between parameters

Soft computing method for predicting pressure drop reduction in crude oil pipelines based on machine learning methods

Contact Info

Product

Resources

About